{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:G6SAYLSLKMJTLQJK7MLDAOIC3O","short_pith_number":"pith:G6SAYLSL","schema_version":"1.0","canonical_sha256":"37a40c2e4b531335c12afb16303902dbad80671ef1b974161bf63efcb20ab411","source":{"kind":"arxiv","id":"2309.08949","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anh Tuan Luu, Bryan Hooi, See-kiong Ng, Yang Deng, Yue Feng, Zekun Li, Zhiyuan Hu","submitted_at":"2023-09-16T10:56:00Z","abstract_excerpt":"Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios. While unlike the general dialogue system which emphasizes the semantic performance, the task-oriented dialogue (ToD) systems aim to achieve the dialogue goal efficiently and successfully in multiple turns. Unfortunately, existing LLM-induced ToD systems lack the direct reward toward the final goal and do not take account of the dialogue proactivity that can strengthen the dialogue eff"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2309.08949","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-16T10:56:00Z","cross_cats_sorted":[],"title_canon_sha256":"701628b0894cd77ae63f1348ad7155e83743169b13642d6a9c7b8e3b80c7c6dd","abstract_canon_sha256":"7822b28b9c56093bdc9d94553553628d000f9e45d046de81a2f09d1be2a2c025"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:51:32.043183Z","signature_b64":"dFOainhEuMquHO6+SqU/qKwk9szKaK2e8tBwHLE3gi2QJIj98qoa6vZ2anT20AOy4K+laZYICP/cvltrUHe7Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37a40c2e4b531335c12afb16303902dbad80671ef1b974161bf63efcb20ab411","last_reissued_at":"2026-07-05T06:51:32.042695Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:51:32.042695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anh Tuan Luu, Bryan Hooi, See-kiong Ng, Yang Deng, Yue Feng, Zekun Li, Zhiyuan Hu","submitted_at":"2023-09-16T10:56:00Z","abstract_excerpt":"Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios. While unlike the general dialogue system which emphasizes the semantic performance, the task-oriented dialogue (ToD) systems aim to achieve the dialogue goal efficiently and successfully in multiple turns. Unfortunately, existing LLM-induced ToD systems lack the direct reward toward the final goal and do not take account of the dialogue proactivity that can strengthen the dialogue eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.08949","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.08949/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2309.08949","created_at":"2026-07-05T06:51:32.042745+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.08949v1","created_at":"2026-07-05T06:51:32.042745+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.08949","created_at":"2026-07-05T06:51:32.042745+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6SAYLSLKMJT","created_at":"2026-07-05T06:51:32.042745+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6SAYLSLKMJTLQJK","created_at":"2026-07-05T06:51:32.042745+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6SAYLSL","created_at":"2026-07-05T06:51:32.042745+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.14584","citing_title":"Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O","json":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O.json","graph_json":"https://pith.science/api/pith-number/G6SAYLSLKMJTLQJK7MLDAOIC3O/graph.json","events_json":"https://pith.science/api/pith-number/G6SAYLSLKMJTLQJK7MLDAOIC3O/events.json","paper":"https://pith.science/paper/G6SAYLSL"},"agent_actions":{"view_html":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O","download_json":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O.json","view_paper":"https://pith.science/paper/G6SAYLSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.08949&json=true","fetch_graph":"https://pith.science/api/pith-number/G6SAYLSLKMJTLQJK7MLDAOIC3O/graph.json","fetch_events":"https://pith.science/api/pith-number/G6SAYLSLKMJTLQJK7MLDAOIC3O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O/action/storage_attestation","attest_author":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O/action/author_attestation","sign_citation":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O/action/citation_signature","submit_replication":"https://pith.science/pith/G6SAYLSLKMJTLQJK7MLDAOIC3O/action/replication_record"}},"created_at":"2026-07-05T06:51:32.042745+00:00","updated_at":"2026-07-05T06:51:32.042745+00:00"}